Amazon Athena vs. Amazon DynamoDB

Best suited for analyzing huge amounts of data by just querying on Amazon Athena. Amazon Athena is also best to integrate with Amazon Quickight for visualization and reporting of data. Easy to work with CSV, JSON, and columnar data formats like Parquet, and ORC. Less appropriate to work with AVRO data format and also stored procedures are not supported in Amazon Athena. The size of a single row is also limited to 32 MB.

Incentivized

It is useful use-case by use-case. For our use case, it was the best and easiest option for the integration as well as development side. It is serverless so no need of deployment and maintenance hustle. It is easy to scale up due to the same functionality. Supports AWS Security features and just a click away for enabling it so security is good.

Incentivized

• Load Balance traffic analysis
• Big data report generation
• Micro services pattern query analysis

Incentivized

• It's very easy to get started, creating a table with a partition/sort key and you're on your way.
• You can scale up and down your read/write IO as needed.
• You can store structured and unstructured data.
• It works great with Web Development as it's JSON based.

Incentivized

• Every dialect of SQL has some missing functions. I wish there was automated GROUP BY options here.
• There are connection problems back to Power BI occasionally.
• If you don't watch certain queries, it's possible that it takes a long time to run and charges you a lot of money.

Incentivized

• Cost model may not be easy to control and may lead to higher costs if not carefully planned
• Indexing may be a cost culprit when not planned, because it's not included on the data costs
• The Query Language may not fulfill everybody's expectations, as it has less features than those of competitors.

Incentivized

It's core to our business, we couldn't survive without it. We use it to drive everything from FTP logins to processing stories and delivering them to clients. It's reliable and easy to query from all of our pipeline services. Integration with things like AWS Lambda makes it easy to trigger events and run code whenever something changes in the database.

Incentivized

Easy to use. Scalable. Gets the job of data warehousing setup done. Using the datalake on S3 has become super convenient.

Incentivized

Functionally, DynamoDB has the features needed to use it. The interface is not as easy to use, which impacts its usability. Being familiar with AWS in general is helpful in understanding the interface, however it would be better if the interface more closely aligned with traditional tools for managing datastores.

Incentivized

While the actual performance of DynamoDB can vary based on workload and region, it is generally highly responsive and well-regarded for delivering low-latency access to data, making it a strong choice for applications with stringent performance requirements. Organizations often choose DynamoDB for its ability to provide a reliable and performant database service, particularly when combined with effective application design and optimization.

Incentivized

I have not had to contact support for this service, however I have had to contact AWS for other services and their support has been good.

Incentivized

Amazon Athena, a product from Amazon, competes with offerings from Google and Microsoft. Overall, I think your database choice depends on some of the other applications you are running at your company. For example, if you are using Microsoft Power BI for reporting needs, you might want to consider going the Azure route.

Incentivized

For our use case, we needed a noSQL that would work with AWS Lambdas of specific parts of the internal web applications. We optimized billing and uses , diversified databases for various parts; so it’s not very expensive.

I have taken one point away due to its size limits. In case the application requires queries, it becomes really complicated to read and write data. When it comes to extremely large data sets such as the case in my company, a third-party logistics company, where huge amount of data is generated on a daily basis, even though the scalability is good, it becomes difficult to manage all the data due to limits.

Incentivized

• It's easy to store and query data on S3. Multiple teams can query the same data to generate their reports. It removes the need for a full-fledged data warehouse for a startup. Saves costs.
• Improved team efficiency on monitoring user activities by easy logging and reporting.
• As the dataset gets heavier on S3, one needs to understand partitioning and that leads to the requirement of expertise.

Incentivized

• Businesses may only pay for the services they actually use thanks to DynamoDB's usage-based pricing approach.
• AWS handles hardware provisioning, data recovery, fault tolerance, patching, and database upgrades for DynamoDB since it is a fully managed database service.
• DynamoDB differs from conventional relational databases in terms of its data model, which might be difficult for developers accustomed to dealing with SQL-based systems.

Incentivized